import argparse
import glob
import os

import numpy as np
from tqdm import tqdm

import torch
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.utils.audio import AudioProcessor
from TTS.utils.generic_utils import load_config

parser = argparse.ArgumentParser(
    description='Compute embedding vectors for each wav file in a dataset. ')
parser.add_argument(
    'model_path',
    type=str,
    help='Path to model outputs (checkpoint, tensorboard etc.).')
parser.add_argument(
    'config_path',
    type=str,
    help='Path to config file for training.',
)
parser.add_argument(
    'data_path',
    type=str,
    help='Data path for wav files - directory or CSV file')
parser.add_argument(
    'output_path',
    type=str,
    help='path for training outputs.')
parser.add_argument(
    '--use_cuda', type=bool, help='flag to set cuda.', default=False
)
parser.add_argument(
    '--separator', type=str, help='Separator used in file if CSV is passed for data_path', default='|'
)
args = parser.parse_args()


c = load_config(args.config_path)
ap = AudioProcessor(**c['audio'])

data_path = args.data_path
split_ext = os.path.splitext(data_path)
sep = args.separator

if len(split_ext) > 0 and split_ext[1].lower() == '.csv':
    # Parse CSV
    print(f'CSV file: {data_path}')
    with open(data_path) as f:
        wav_path = os.path.join(os.path.dirname(data_path), 'wavs')
        wav_files = []
        print(f'Separator is: {sep}')
        for line in f:
            components = line.split(sep)
            if len(components) != 2:
                print("Invalid line")
                continue
            wav_file = os.path.join(wav_path, components[0] + '.wav')
            #print(f'wav_file: {wav_file}')
            if os.path.exists(wav_file):
                wav_files.append(wav_file)
    print(f'Count of wavs imported: {len(wav_files)}')
else:
    # Parse all wav files in data_path
    wav_path = data_path
    wav_files = glob.glob(data_path + '/**/*.wav', recursive=True)

output_files = [wav_file.replace(wav_path, args.output_path).replace(
    '.wav', '.npy') for wav_file in wav_files]

for output_file in output_files:
    os.makedirs(os.path.dirname(output_file), exist_ok=True)

model = SpeakerEncoder(**c.model)
model.load_state_dict(torch.load(args.model_path)['model'])
model.eval()
if args.use_cuda:
    model.cuda()

for idx, wav_file in enumerate(tqdm(wav_files)):
    mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T
    mel_spec = torch.FloatTensor(mel_spec[None, :, :])
    if args.use_cuda:
        mel_spec = mel_spec.cuda()
    embedd = model.compute_embedding(mel_spec)
    np.save(output_files[idx], embedd.detach().cpu().numpy())
